The analysis on \(\mathcal {L}_{\rm STA}\)  from KL-divergence indicates that the iteratively generated perturbations are in small variance and updated in a more consistent direction. We visualize the perturbations in different iterations in Fig. REF  to see how perturbations vary in practice. We send an input fundus image to the ResNet-50 [1]} integrated with a graph convolutional network [2]} framework for image classification. The perturbations are generated via Eq. REF  and Eq. REF  independently.
